We will adopt a data-driven approach using machine learning algorithms for analyses of longitudinal clinical data collected over 50 years in the Baltimore Longitudinal Study of Aging (BLSA).This study will test whether distinct changes in the temporal sequence of laboratory-derived measurements of human physiology and co-morbid medical conditions will predict differential risk of Alzheimer's disease in cognitively normal older individuals. If we are successful, we hope that we will discover novel insights into avenues for prevention and/or treatment of AD.